Microgrid short-term load prediction method based on empirical mode decomposition

Abstract

The invention relates to a microgrid short-term load prediction method based on empirical mode decomposition, and the method comprises the steps: S1, collecting original load data, carrying out the preprocessing, and obtaining a total load curve; S2, decomposing the total load curve into a trend load curve and a fluctuation load curve through empirical mode decomposition; S3, constructing a grey prediction model based on an amplitude compression method to obtain a trend load prediction value; S4, establishing an MIC historical matrix considering load similar day relevance and a UTCI historicallibrary including meteorological and geographical location factors, and obtaining a fluctuation load prediction value; and S5, reconstructing the trend load prediction value and the fluctuation loadprediction value, and obtaining a short-term load prediction value to control the working state of the distributed power supply in the microgrid. Compared with the prior art, the method has the advantages that the influence of meteorological and geographical location factors on load prediction is comprehensively considered on the basis of analyzing load characteristics, high prediction accuracy isachieved, and prediction speed is increased.

Description

technical field [0001] The invention relates to the technical field of load forecasting, in particular to a short-term load forecasting method for a microgrid based on empirical mode decomposition. Background technique [0002] Microgrid short-term load forecasting is the key to realizing microgrid intelligent scheduling and optimizing energy efficiency management. Controlling the output of distributed power sources in microgrids according to short-term load forecasting values ​​is of great significance for safe power supply, economic cost reduction, and stable operation of microgrids. . The load of the user-side microgrid has the characteristics of a small base and is easily affected by external factors and fluctuates violently, and the nonlinear complexity of its curve is much higher than that of the large power grid load curve. In actual forecasting, the existing forecasting models often result in insufficient forecasting accuracy due to ignoring the influence of multipl...

AI Technical Summary

Problems solved by technology

Most of the above methods predict the power load curve as a whole when modeling, resulting in slow prediction speed and long prediction time; and the comprehensive influence of various meteorological factors is not considered, resulting in low prediction accuracy

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